A Black Hole Attack Model for Reactive Ad-Hoc Protocols

Net-Centric Warfare places the network in the center of all operations, making it a critical resource to attack and defend during wartime. This thesis examines one particular network attack, the Black Hole attack, to determine if an analytical model can be used to predict the impact of this attack on ad-hoc networks. An analytical Black Hole attack model is developed for reactive ad-hoc network protocols DSR and AODV. To simplify topology analysis, a hypercube topology is used to approximate ad-hoc topologies that have the same average node degree. An experiment is conducted to compare the predicted results of the analytical model against simulated Black Hole attacks on a variety of ad-hoc networks. The results show that the model describes the general order of growth in Black Hole attacks as a function of the number of Black Holes in a given network. The model accuracy maximizes when both the hypercube approximation matches the average degree and number of nodes of the ad-hoc topology. For this case, the model falls within the 95% confidence intervals of the estimated network performance loss for 17 out of 20 measured scenarios for AODV and 7 out of 20 for DSR

Faulty node repair and dynamically spawned black hole search

New threats to networks are constantly arising. This justifies protecting network assets and mitigating the risk associated with attacks. In a distributed environment, researchers aim, in particular, at eliminating faulty network entities. More specifically, much research has been conducted on locating a single static black hole, which is defined as a network site whose existence is known a priori and that disposes of any incoming data without leaving any trace of this occurrence. However, the prevalence of faulty nodes requires an algorithm able to (a) identify faulty nodes that can be repaired without human intervention and (b) locate black holes, which are taken to be faulty nodes whose repair does require human intervention. In this paper, we consider a specific attack model that involves multiple faulty nodes that can be repaired by mobile software agents, as well as a virus v that can infect a previously repaired faulty node and turn it into a black hole. We refer to the task of repairing multiple faulty nodes and pointing out the location of the black hole as the Faulty Node Repair and Dynamically Spawned Black Hole Search. Wefirst analyze the attack model we put forth. We then explain (a) how to identify whether a node is either (1) a normal node or (2) a repairable faulty node or (3) the black hole that has been infected by virus v during the search/repair process and, (b) how to perform the correct relevant actions. These two steps constitute a complex task, which, we explain, significantly differs from the traditional Black Hole Search. We continue by proposing an algorithm to solve this problem in an

Searching for black holes in subways.

Abstract Current mobile agent algorithms for mapping faults in computer networks assume that the network is static. However, for large classes of highly dynamic networks (e.g., wireless mobile ad hoc networks, sensor networks, vehicular networks), the topology changes as a function of time. These networks, called delay-tolerant, challenged, opportunistic, etc., have never been investigated with regard to locating faults. We consider a subclass of these networks modelled on an urban subway system. We examine the problem of creating a map of such a subway. More precisely, we study the problem of a team of asynchronous computational entities (the mapping agents) determining the location of black holes in a highly dynamic graph, whose edges are defined by the asynchronous movements of mobile entities (the subway carriers). We determine necessary conditions for the problem to be solvable. We then present and analyze a solution protocol; we show that our algorithm solves the fault mapping problem in subway networks with the minimum number of agents possible, k = γ + 1, where γ is the number of carrier stops at black holes. The number of carrier moves between stations required by the algorithm in the worst case is , where n C is the number of subway trains, and l R is the length of the subway route with the most stops. We establish lower bounds showing that this bound is tight. Thus, our protocol is both agent-optimal and move-optimal

Synchronous Black Hole Search in Directed Graphs

International audienceThe paper considers a team of robots which has to explore a graph G where some nodes can be harmful. Robots are initially located at the so called home base node. The dangerous nodes are the so called black hole nodes, and once a robot enters in one of them, it is destroyed. The goal is to find a strategy in order to explore G in such a way that the minimum number of robots is wasted. The exploration ends if there is at least one surviving robot which knows all the edges leading to the black holes. As many variations of the problem have been considered so far, the solution and its measure heavily depend on the initial knowledge and the capabilities of the robots. In this paper, we assume that G is a directed graph, the robots are associated with unique identifiers, they know the number of nodes n of G (or at least an upper bound on n), and they know the number of edges Delta leading to the black holes. Each node is associated with a whiteboard where robots can read and write information in a mutual exclusive way. A recently posed question [Czyzowicz et al., Proc. SIROCCO'09] is whether some number of robots, expressed as a function of parameter Delta only, is suffi- cient to detect black holes in directed graphs of arbitrarily large order n. We give a positive answer to this question for the synchronous case, i.e., when the robots share a common clock, showing that O(Delta* 2^Delta) robots are sufficient to solve the problem. This bound is nearly tight, since it is known that at least 2^Delta robots are required for some instances. Quite surprisingly, we also show that unlike in the case of undirected graphs, for the directed version of the problem, synchronization can sometimes make a difference: for Delta = 2, in the synchronous case 4 robots are always sufficient, whereas in the asynchronous case at least 5 robots are sometimes required